Project #03: Visualizing and Maintaining the Green Canopy of NYC

Introduction

This project will explore the NYC TreeMap data set and creating visualizations of NYC’s urban fabric. Based on these visualizations, I will propose a new program for the NYC Parks Department that attempts to make the benefits of NYC trees available to all New Yorkers.

Data Acquisition

Retrieve boundaries of districts

library(sf)
library(tidyverse)
library(dplyr)
library(httr2)


highlight <- function(x) {
  paste0('<span style="color:#e41a1c;"><b>', x, "</b></span>")
}

# Create data directory if it doesn't exist
if (!dir.exists("data/mp03")) {
  dir.create("data/mp03", recursive = TRUE, showWarnings = FALSE)
  cat("✓ Created data/mp03 directory\n")
}

get_council_districts <- function() {
  cat("\n=== Downloading NYC Council District Boundaries ===\n")

  # Use ArcGIS Hub - NYC Planning's official data portal
  api_url <- "https://services5.arcgis.com/GfwWNkhOj9bNBqoJ/arcgis/rest/services/NYC_City_Council_Districts/FeatureServer/0/query"
  geojson_file <- "data/mp03/council_districts_arcgis.geojson"

  # Download only if file doesn't exist (responsible downloading)
  if (!file.exists(geojson_file)) {
    cat("Downloading from ArcGIS Hub...\n")

    resp <- request(api_url) %>%
      req_url_query(
        where = "1=1", # Get all records
        outFields = "*", # All fields
        returnGeometry = "true", # Include geometries
        f = "geojson" # GeoJSON format
      ) %>%
      req_headers(`User-Agent` = "Mozilla/5.0 (Educational Project)") %>%
      req_perform()

    writeBin(resp_body_raw(resp), geojson_file)
    cat("✓ Downloaded GeoJSON file\n")
  } else {
    cat("✓ GeoJSON file already exists\n")
  }

  # Read GeoJSON
  districts <- st_read(geojson_file, quiet = TRUE)

  # Verify geometries are valid
  if (all(st_is_empty(districts))) {
    stop("ERROR: Downloaded data has empty geometries!")
  }

  # Transform to WGS84 coordinate system (as required)
  districts <- st_transform(districts, crs = "WGS84")


  return(districts)
}
districts <- get_council_districts()

=== Downloading NYC Council District Boundaries ===
✓ GeoJSON file already exists

Download Tree Points

library(sf)
library(dplyr)
library(httr2)
library(fs)

get_tree_points <- function(limit = 50000) {
  cat("\n=== Downloading NYC Tree Points ===\n")

  # Base URL for the NYC OpenData Tree Points API (Socrata)
  base_url <- "https://data.cityofnewyork.us/resource/hn5i-inap.geojson"

  # Initialize pagination variables
  offset <- 0
  all_files <- list()
  page_num <- 1

  # Page through entire dataset using $limit and $offset parameters
  repeat {
    filename <- file.path("data/mp03", sprintf("trees_page_%03d.geojson", page_num))

    # Check if file already exists (avoid re-downloading)
    if (file.exists(filename)) {
      cat(sprintf("✓ Page %d already downloaded\n", page_num))
      all_files[[page_num]] <- filename

      # Check if we got fewer results (indicates end of dataset)
      test_data <- st_read(filename, quiet = TRUE)
      if (nrow(test_data) < limit) {
        cat("✓ Reached end of dataset\n")
        break
      }

      offset <- offset + limit
      page_num <- page_num + 1
      next
    }

    # Download this page using httr2
    cat(sprintf("Downloading page %d (offset: %d)...\n", page_num, offset))

    resp <- request(base_url) %>%
      req_url_query(`$limit` = limit, `$offset` = offset) %>%
      req_headers(`User-Agent` = "Mozilla/5.0 (Educational Project)") %>%
      req_retry(max_tries = 3) %>%
      req_perform()

    # Save response to file
    writeBin(resp_body_raw(resp), filename)
    all_files[[page_num]] <- filename
    cat(sprintf("✓ Saved page %d\n", page_num))

    # Read to check how many records we got
    current_data <- st_read(filename, quiet = TRUE)
    n_records <- nrow(current_data)
    cat(sprintf("  Retrieved %d records\n", n_records))

    # If we got fewer than limit, we've reached the end
    if (n_records < limit) {
      cat("✓ Reached end of dataset\n")
      break
    }

    # Update for next iteration
    offset <- offset + limit
    page_num <- page_num + 1

    # Be polite - add a small delay between requests
    Sys.sleep(0.5)
  }

  # Read and combine all GeoJSON files
  cat("\nCombining all tree point files...\n")
  tree_list <- lapply(all_files, function(f) {
    st_read(f, quiet = TRUE)
  })

  trees <- bind_rows(tree_list)
  cat("✓ Successfully loaded", format(nrow(trees), big.mark = ","), "trees\n")

  return(trees)
}

trees <- get_tree_points()

=== Downloading NYC Tree Points ===
✓ Page 1 already downloaded
✓ Page 2 already downloaded
✓ Page 3 already downloaded
✓ Page 4 already downloaded
✓ Page 5 already downloaded
✓ Page 6 already downloaded
✓ Page 7 already downloaded
✓ Page 8 already downloaded
✓ Page 9 already downloaded
✓ Page 10 already downloaded
✓ Page 11 already downloaded
✓ Page 12 already downloaded
✓ Page 13 already downloaded
✓ Page 14 already downloaded
✓ Page 15 already downloaded
✓ Page 16 already downloaded
✓ Page 17 already downloaded
✓ Page 18 already downloaded
✓ Page 19 already downloaded
✓ Page 20 already downloaded
✓ Page 21 already downloaded
✓ Page 22 already downloaded
✓ Reached end of dataset

Combining all tree point files...
✓ Successfully loaded 1,093,439 trees

Data Integration and Initial Exploration

Mapping NYC Trees

Here we’re mapping NYC trees.

library(ggplot2)
library(dplyr)
library(sf)


plot_all <- FALSE
sample_size <- 5000  # smaller for testing

trees_to_plot <- slice_sample(trees, n = min(sample_size, nrow(trees)))

# Plot
ggplot() +
  geom_sf(data = districts, fill = NA, color = "grey60", linewidth = 0.3) +
  geom_sf(data = trees_to_plot, color = "#228B22", alpha = 0.12, size = 0.06) +
  coord_sf(expand = FALSE) +
  labs(
    title = "NYC Tree Points",
    subtitle = if (plot_all) {
      paste("All trees included:", format(nrow(trees), big.mark = ","))
    } else {
      paste("Sample of", format(nrow(trees_to_plot), big.mark = ","), 
            "trees from total of", format(nrow(trees), big.mark = ","))
    },
    x = NULL, y = NULL
  ) +
  theme_minimal(base_size = 11) +
  theme(panel.grid.major = element_line(linewidth = 0.2, colour = "grey90"))

# app.R
library(dplyr)
library(leaflet)
library(htmltools)

District Analyses of Trees

Here I’m joining the districts and trees together to do more analysis. This includes which district has the most trees, highest density, highest fraction of dead trees, and most common tree species. I will be using interactive data visualizations for this as well.

library(sf)
library(dplyr)
library(gt)

# Join tree points onto district polygons
trees_districts <- st_join(trees, districts, join = st_intersects, left = TRUE)

# Check that expected columns exist
if (!"genusspecies" %in% names(trees_districts)) {
  trees_districts$genusspecies <- NA  # add empty column if missing
}

if (!"CounDist" %in% names(trees_districts)) {
  trees_districts$CounDist <- NA  # add empty column if missing
}

# Summarize tree counts per district (numeric CounDist)
trees_per_district <- trees_districts %>%
  st_set_geometry(NULL) %>%  # drop geometry for table
  group_by(CounDist) %>%
  summarise(Trees = n(), .groups = "drop") %>%
  arrange(desc(Trees))

# Take only the top district
top_district <- trees_per_district %>% slice_head(n = 1)

1. Which council district has the most trees?

# Make a table and highlight District 51
top_district %>%
  gt() %>%
  tab_style(
    style = cell_fill(color = "lightblue"),
    locations = cells_body(columns = c("CounDist"), rows = CounDist == 51)
  ) %>%
  cols_label(
    CounDist = "Council District",
    Trees = "Number of Trees"
  ) %>%
  tab_header(
    title = "NYC District with the Most Trees",
    subtitle = paste("Total trees in this district:", top_district$Trees)
  )

NYC District with the Most Trees
Total trees in this district: 70928
Council District	Number of Trees
51	70928

NoteKey Finding

District 51 is the one with the most trees.

2. Which council district has the highest density of trees?

# Add Shape_Area to your summarized table

# 2. Summarize tree counts per district

trees_density <- trees_per_district %>% left_join( districts %>% st_set_geometry(NULL) %>% select(CounDist, Shape__Area), by = "CounDist" ) %>% mutate(Tree_Density = Trees / Shape__Area) %>% arrange(desc(Tree_Density)) # See the top district by tree density trees_density %>% slice_head(n = 2)


trees_density %>%
  slice_head(n = 3) %>%  # top 3 by density
  select(CounDist, Tree_Density) %>%
  gt() %>%
  tab_header(
    title = "Top 3 NYC Districts with Highest Tree Density"
  ) %>%
  cols_label(
    CounDist = "Council District",
    Tree_Density = "Tree Density"
  ) %>%
  fmt_number(
    columns = "Tree_Density",
    decimals = 4  # adjust decimals as needed
  ) %>%
  tab_style(
    style = cell_fill(color = "lightblue"),
    locations = cells_body(
      columns = everything(),
      rows = CounDist %in% c(7, 39)
    )
  )

Top 3 NYC Districts with Highest Tree Density
Council District	Tree Density
7	0.0003
39	0.0003
2	0.0002

NoteKey Finding

The council district with the highest tree density is 7 and 39.

3. Which district has highest fraction of dead trees out of all trees?

# Summarize dead fraction per district
dead_fraction <- trees_districts %>%
  st_set_geometry(NULL) %>%  # drop geometry
  group_by(CounDist) %>% # group by district as character
  summarise(
    Total_Trees = n(),
    Dead_Trees  = sum(tpcondition == "Dead", na.rm = TRUE),
    Dead_Fraction = Dead_Trees / Total_Trees
  ) %>%
  arrange(desc(Dead_Trees))

# Get top 5 districts by dead fraction
top_districts <- dead_fraction %>%
  slice_head(n = 5)

# Identify the top district for highlighting
top_district <- top_districts$CounDist[1]

# Create the table
library(gt)

top_districts %>%
  select(CounDist, Total_Trees, Dead_Trees, Dead_Fraction) %>%
  gt() %>%
  cols_label(
    CounDist  = "Council District",
    Total_Trees   = "Total Trees",
    Dead_Trees    = "Dead Trees",
    Dead_Fraction = "Dead Fraction"
  ) %>%
  fmt_number(
    columns = c(Dead_Fraction),
    decimals = 2
  ) %>%
  tab_header(
    title = "Top 5 NYC Council Districts by Dead Tree Fraction"
  ) %>%
  tab_style(
    style = cell_fill(color = "lightblue"),
    locations = cells_body(
      rows = CounDist == top_district
    )
  )

Top 5 NYC Council Districts by Dead Tree Fraction
Council District	Total Trees	Dead Trees	Dead Fraction
51	70928	9147	0.13
50	52438	7041	0.13
19	49832	6335	0.13
23	44807	5828	0.13
49	35027	4569	0.13

NoteKey Finding

The district with the highest fraction of dead trees is 51

4. What is the most common tree species in Manhattan?

# Add a borough column (you already did this)
trees_districts <- trees_districts %>%
  mutate(
    Borough = case_when(
      CounDist >= 1  & CounDist <= 10 ~ "Manhattan",
      CounDist >= 11 & CounDist <= 18 ~ "Bronx",
      CounDist >= 19 & CounDist <= 32 ~ "Queens",
      CounDist >= 33 & CounDist <= 48 ~ "Brooklyn",
      CounDist >= 49 & CounDist <= 51 ~ "Staten Island",
      TRUE ~ "Other"
    )
  )

# Create most_common_manhattan table
most_common_manhattan <- trees_districts %>%
  filter(Borough == "Manhattan") %>%
  st_set_geometry(NULL) %>%  # drop geometry for table
  group_by(genusspecies) %>%
  summarise(n = n()) %>%
  arrange(desc(n)) %>%
  slice_head(n = 1)  # only top species

# Now display as a gt table
most_common_manhattan %>%
  gt() %>%
  cols_label(
    genusspecies = "Tree Species",
    n = "Number of Trees"
  ) %>%
  tab_header(
    title = "Most Common Tree Species in Manhattan"
  )

Most Common Tree Species in Manhattan
Tree Species	Number of Trees
Gleditsia triacanthos var. inermis - Thornless honeylocust	17311

NoteKey Finding

The species in manhattan with most trees is Gleditsia triacanthos var. inermis - Thornless honeylocust.

5. What is the species of the tree closest to Baruch’s campus?

library(units)
library(sf)
library(dplyr)

# Function to create a point
new_st_point <- function(lat, lon) {
  st_sfc(st_point(c(lon, lat))) |>  # longitude first
    st_set_crs("WGS84")
}

# Baruch College coordinates
baruch_point <- new_st_point(lat = 40.7401, lon = -73.9832)

# Project both trees and Baruch point to a planar CRS for distance in meters
trees_proj <- st_transform(trees, 2263)
baruch_proj <- st_transform(baruch_point, 2263)

# Compute distances in meters
trees_distance <- trees_proj %>%
  mutate(distance_m = as.numeric(st_distance(geometry, baruch_proj)))

# Find closest tree
closest_tree <- trees_distance %>%
  slice_min(distance_m, n = 1)

NoteKey Finding

The tree closest to Baruch College is a Quercus acutissima - sawtooth oak, located approximately 100.5 meters away.

library(leaflet)
library(sf)

library(leaflet)
library(dplyr)

# Ensure trees is an sf object (with geometry column)
class(trees)  # should include "sf"

[1] "sf"         "data.frame"

# If not, convert using your geometry column:
# trees <- st_as_sf(trees, coords = c("longitude", "latitude"), crs = 4326)

# Ensure closest_tree keeps geometry, do NOT drop it
# closest_tree <- trees_distance %>% slice_min(distance_m, n = 1)

# Transform everything to WGS84 for Leaflet
trees_wgs <- st_transform(trees, 4326)
baruch_wgs <- st_transform(baruch_point, 4326)
closest_tree_wgs <- st_transform(closest_tree, 4326)

# Optional: create a small buffer around Baruch for context (e.g., 100m)
buffer_wgs <- st_transform(
  st_buffer(st_transform(baruch_point, 2263), 100),
  4326
)

leaflet() %>%
  addProviderTiles(providers$OpenStreetMap) %>%
  addPolygons(data = buffer_wgs, color = "red", weight = 2, fill = FALSE, group = "Buffer") %>%
  addCircleMarkers(
    data = trees_wgs,
    radius = 2,
    color = "green",
    fillOpacity = 0.3,
    group = "All Trees"
  ) %>%
  addCircleMarkers(
    data = closest_tree_wgs,
    radius = 8,
    color = "darkgreen",
    fillColor = "darkgreen",
    fillOpacity = 1,
    popup = paste0("Closest Tree: ", closest_tree_wgs$genusspecies),
    group = "Closest Tree"
  ) %>%
  addMarkers(
    data = baruch_wgs,
    popup = "Baruch College",
    label = "Baruch College"
  ) %>%
  addLayersControl(
    overlayGroups = c("Buffer", "All Trees", "Closest Tree"),
    options = layersControlOptions(collapsed = FALSE)
  ) %>%
  setView(lng = -73.9832, lat = 40.7401, zoom = 17)

NYC Parks Proposal

library(htmltools)
library(tidyverse)
library(dplyr)
library(leaflet)
library(sf)

# --- Summarize trees by district -------------------------------------------------
FT2_PER_SQMI <- 5280^2

# Use the actual status column
status_col <- "tpcondition"

dist_stats <- trees_districts %>%
  st_set_geometry(NULL) %>%  # drop geometry for summarizing
  group_by(CounDist) %>%
  summarise(
    n_trees = n(),
    n_dead = sum(tolower(.data[[status_col]]) == "dead", na.rm = TRUE),
    .groups = "drop"
  )

# Add area to compute trees per sq mi
dist_area <- districts %>%
  st_set_geometry(NULL) %>%
  select(CounDist = CounDist, area_ft2 = Shape__Area)

dist_stats <- dist_stats %>%
  left_join(dist_area, by = "CounDist") %>%
  mutate(
    frac_dead = n_dead / n_trees,
    trees_sqmi = n_trees / (area_ft2 / FT2_PER_SQMI)
  )

# Attach stats to districts
districts_popup <- districts %>%
  left_join(dist_stats, by = c("CounDist" = "CounDist"))

# HTML popup function
popup_html <- function(cd, n, dead, frac, dens) {
  HTML(sprintf(
    "<b>District %s</b><br/>Trees: %s<br/>Dead: %s (%.1f%%)<br/>Trees / sq mi: %s",
    cd,
    format(n, big.mark = ","),
    ifelse(is.na(dead), "NA", format(dead, big.mark = ",")),
    ifelse(is.na(frac), 0, 100 * frac),
    format(round(dens, 1), big.mark = ",")
  ))
}

# Leaflet map
leaflet(options = leafletOptions(minZoom = 9)) %>%
  addProviderTiles(providers$CartoDB.Positron) %>%
  addPolygons(
    data = st_simplify(districts_popup, dTolerance = 50),
    color = "grey40", weight = 1, fill = FALSE,
    label = ~ paste("District", CounDist),
    popup = ~ popup_html(CounDist, n_trees, n_dead, frac_dead, trees_sqmi),
    highlightOptions = highlightOptions(weight = 2, color = "#000", bringToFront = TRUE)
  )

Government Project Design - Proposal

City Council Propose Additional Funding - Replacing Dead Trees in District 32

Project Overview

This initiative focuses on replacing dead trees and increasing the green space density in District 32. The goal is to enhance the neighborhood’s appearance, give the area a transformation to attract more visitors and residents, as well as supporting community well-being.

The project will contribute to a larger effort to improve Rockaway’s public spaces, making it a more attractive destination for beach-goers and creating a healthier, greener environment for residents throughout the year.

District Selection: A comparative analysis across districts shows that District 32 has a significant high dead-tree share (14% of it’s total tree population). Focusing on giving resources to replacing trees can make a great impact on the affected neighborhoods.

Scope and Goals - Replace 100 dead trees to reduce hazards and make space for healthy trees - Plant 200 new trees, with a mix of tree species to increase green space density and biodiversity - Work done in District 32, around Rockaway Beach

Supporting Visuals While comparing districts, we found that as of today, District 32 currently has the highest dead-tree fractions in the city, making it a priority for target action.

# Summarize dead trees per district
dead_fraction <- trees_districts %>%
  st_set_geometry(NULL) %>%                  # drop geometry
  mutate(CounDist = as.character(CounDist)) %>%  # create column
  group_by(CounDist) %>%
  summarise(
    Total_Trees   = n(),
    Dead_Trees    = sum(tpcondition == "Dead", na.rm = TRUE),
    Dead_Fraction = Dead_Trees / Total_Trees,
    .groups = "drop"
  ) %>%
  arrange(desc(Dead_Trees))

# Top 5 districts
top_districts <- dead_fraction %>% slice_head(n = 5)

# Identify the top district for highlighting
top_district <- top_districts$CounDist[1]

# Create the table with gt
top_districts %>%
  select(CounDist, Total_Trees, Dead_Trees, Dead_Fraction) %>%
  gt() %>%
  cols_label(
    CounDist  = "Council District",
    Total_Trees   = "Total Trees",
    Dead_Trees    = "Dead Trees",
    Dead_Fraction = "Dead Tree Share"
  ) %>%
  fmt_number(
    columns = Dead_Fraction,
    decimals = 2
  ) %>%
  tab_header(
    title = "NYC 5 Highest Council Districts by Dead Tree Share"
  ) %>%
  tab_style(
    style = cell_fill(color = "lightpink"),
    locations = cells_body(
      rows = CounDist == top_district
    )
  )

NYC 5 Highest Council Districts by Dead Tree Share
Council District	Total Trees	Dead Trees	Dead Tree Share
51	70928	9147	0.13
50	52438	7041	0.13
19	49832	6335	0.13
23	44807	5828	0.13
49	35027	4569	0.13

This is a comparison chart showing dead-tree fractions across all districts in NYC, highlighting District 32 as the highest priority area.

library(ggplot2)
library(dplyr)


# Summarize dead trees per district
dead_fraction <- trees_districts %>%
  st_set_geometry(NULL) %>%                  # drop geometry
  mutate(CounDist = as.character(CounDist)) %>%  # create column
  group_by(CounDist) %>%
  summarise(
    Total_Trees   = n(),
    Dead_Trees    = sum(tpcondition == "Dead", na.rm = TRUE),
    Dead_Fraction = Dead_Trees / Total_Trees,
    .groups = "drop"
  ) %>%
  arrange(desc(Dead_Trees))

dead_fraction_clean <- dead_fraction %>%
  filter(!is.na(CounDist)) %>%  # remove NA
  mutate(highlight = ifelse(CounDist == "32", "District 32", "Other Districts"))

ggplot(dead_fraction_clean, aes(x = reorder(CounDist, Dead_Fraction), y = Dead_Fraction, fill = highlight)) +
  geom_col() +
  coord_flip() +
  scale_fill_manual(values = c("District 32" = "red", "Other Districts" = "grey80")) +
  labs(
    title = "Dead Tree Share Across All NYC Districts",
    x = "Council District",
    y = "Dead Tree Share",
    fill = ""
  ) +
  theme_minimal() +
  theme(
    axis.text.y = element_text(size = 6)  # smaller font to reduce clutter
  )

This is a zoomed-in map of District 32 highlighting the dead and alive trees targeted for replacement.

# Summarize dead trees per district
dead_fraction <- trees_districts %>%
  st_set_geometry(NULL) %>%                  # drop geometry
  mutate(CounDist = as.character(CounDist)) %>%  # create column
  group_by(CounDist) %>%
  summarise(
    Total_Trees   = n(),
    Dead_Trees    = sum(tpcondition == "Dead", na.rm = TRUE),
    Dead_Fraction = Dead_Trees / Total_Trees,
    .groups = "drop"
  ) %>%
  arrange(desc(Dead_Trees))

# Filter for District 32
trees_d32 <- trees_districts %>% filter(CounDist == "32")

# Optional: just dead trees
dead_trees_d32 <- trees_d32 %>% filter(tpcondition == "Dead")

# Transform to WGS84 for leaflet
trees_wgs <- st_transform(trees_d32, 4326)
dead_wgs  <- st_transform(dead_trees_d32, 4326)

# Leaflet map
leaflet() %>%
  addProviderTiles(providers$CartoDB.Positron) %>%
  addCircleMarkers(
    data = trees_wgs,
    radius = 2,
    color = "green",
    fillOpacity = 0.9,
    group = "All Trees"
  ) %>%
  addCircleMarkers(
  data = dead_wgs,
  radius = 4,
  color = "darkred",
  fillColor = "darkred",
  fillOpacity = 0.1,
  opacity = 1,
  popup = ~genusspecies,
  group = "Dead Trees"
)

Conclusion This initiative is part of a broader effort to improve Rockaway’s public spaces, making it safer, greener, and more attractive year-round. By addressing the urgent need for tree replacement in District 32, the project supports both environmental resilience and community well-being.